Check data was anomalized

src/pytimetk/plot/plot_anomalies.py

@@ -8,7 +8,7 @@
 
 from pytimetk.plot.plot_timeseries import plot_timeseries
 from pytimetk.utils.plot_helpers import hex_to_rgba, rgba_to_hex, parse_rgba
-from pytimetk.utils.checks import check_dataframe_or_groupby, check_date_column
+from pytimetk.utils.checks import check_dataframe_or_groupby, check_date_column, check_anomalize_data
 from pytimetk.plot.theme import theme_timetk
 
 
@@ -247,6 +247,9 @@ def plot_anomalies(
     check_dataframe_or_groupby(data)
     check_date_column(data, date_column)
 
+    # Check data was anomalized first
+    check_anomalize_data(data)
+
     # Handle line_size
     if line_size is None:
         if engine == 'plotnine':

src/pytimetk/utils/checks.py

@@ -6,6 +6,30 @@
 
 from typing import Union, List
 
+def check_anomalize_data(data: Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]) -> None:
+
+    if isinstance(data, pd.core.groupby.generic.DataFrameGroupBy):
+        data = data.obj
+
+    expected_colnames = [
+        'observed',
+        'seasonal',
+        'seasadj',
+        'trend',
+        'remainder',
+        'anomaly',
+        'anomaly_score',
+        'anomaly_direction',
+        'recomposed_l1',
+        'recomposed_l2',
+        'observed_clean'
+    ]
+
+    if not all([column in data.columns for column in expected_colnames]):
+        raise ValueError(f"data does not have required colnames: {expected_colnames}. Did you run `anomalize()`?")
+
+    return None
+
 def check_dataframe_or_groupby(data: Union[pd.DataFrame, pd.core.groupby.generic.DataFrameGroupBy]) -> None:
 
     if not isinstance(data, pd.DataFrame):